class_docstr: AutoGluon model wrapper to the TabPFN model: https://github.com/automl/TabPFN
  
  Paper: "TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second"
  Authors: Noah Hollmann, Samuel Müller, Katharina Eggensperger, and Frank Hutter
  
  TabPFN is a viable model option when inference speed is not a concern,
  and the number of rows of training data is less than 10,000.
  
  Additionally, TabPFN is only available for classification tasks with up to 10 classes and 100 features.
  
  To use this model, `tabpfn` must be installed.
  To install TabPFN, you can run `pip install autogluon.tabular[tabpfn]` or `pip install tabpfn`.
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docstr: By default, we only use 1 ensemble configurations to speed up inference times.
  Increase the value to improve model quality while linearly increasing inference time.
  
  Model quality improvement diminishes significantly beyond `N_ensemble_configurations=8`.
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code: def _set_default_params(self):
      """
      By default, we only use 1 ensemble configurations to speed up inference times.
      Increase the value to improve model quality while linearly increasing inference time.
  
      Model quality improvement diminishes significantly beyond `N_ensemble_configurations=8`.
      """
      default_params = {
          "N_ensemble_configurations": 1,
      }
      for param, val in default_params.items():
          self._set_default_param_value(param, val)
  
file_path: autogluon/tabular/src/autogluon/tabular/models/tabpfn/tabpfn_model.py
imports: import numpy
  import pandas
  from autogluon.core.constants import BINARY, MULTICLASS
  from autogluon.core.models import AbstractModel
  from autogluon.core.utils import generate_train_test_split
  from autogluon.features.generators import LabelEncoderFeatureGenerator
label_desc: Use image node_id 5 for calling the TabPFNModel obj's underlying member method code with example usage: obj._set_default_params() without return types